AUTHOR=Zhu Bin , Zhao Jianlei , Cao Mingnan , Du Wanliang , Yang Liuqing , Su Mingliang , Tian Yue , Wu Mingfen , Wu Tingxi , Wang Manxia , Zhao Xingquan , Zhao Zhigang 

TITLE=Predicting 1-Hour Thrombolysis Effect of r-tPA in Patients With Acute Ischemic Stroke Using Machine Learning Algorithm

JOURNAL=Frontiers in Pharmacology

VOLUME=12

YEAR=2022

URL=https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2021.759782

DOI=10.3389/fphar.2021.759782

ISSN=1663-9812

ABSTRACT=<p><bold>Background:</bold> Thrombolysis with r-tPA is recommended for patients after acute ischemic stroke (AIS) within 4.5 h of symptom onset. However, only a few patients benefit from this therapeutic regimen. Thus, we aimed to develop an interpretable machine learning (ML)–based model to predict the thrombolysis effect of r-tPA at the super-early stage.</p><p><bold>Methods:</bold> A total of 353 patients with AIS were divided into training and test data sets. We then used six ML algorithms and a recursive feature elimination (RFE) method to explore the relationship among the clinical variables along with the NIH stroke scale score 1 h after thrombolysis treatment. Shapley additive explanations and local interpretable model–agnostic explanation algorithms were applied to interpret the ML models and determine the importance of the selected features.</p><p><bold>Results:</bold> Altogether, 353 patients with an average age of 63.0 (56.0–71.0) years were enrolled in the study. Of these patients, 156 showed a favorable thrombolysis effect and 197 showed an unfavorable effect. A total of 14 variables were enrolled in the modeling, and 6 ML algorithms were used to predict the thrombolysis effect. After RFE screening, seven variables under the gradient boosting decision tree (GBDT) model (area under the curve = 0.81, specificity = 0.61, sensitivity = 0.9, and F1 score = 0.79) demonstrated the best performance. Of the seven variables, activated partial thromboplastin clotting time (time), B-type natriuretic peptide, and fibrin degradation products were the three most important clinical characteristics that might influence r-tPA efficiency.</p><p><bold>Conclusion:</bold> This study demonstrated that the GBDT model with the seven variables could better predict the early thrombolysis effect of r-tPA.</p>